Synopsis

We build a random forest classifier model on the Weight Lifting Exercises Dataset1 to predict how well an activity is done.

About the dataset:

Packages

Load the R packages needed further. It’s also good to set system locale to avoid problems related with system differences between regions.

library(data.table, quietly = T, warn.conflicts = F)
library(caret, quietly = T,warn.conflicts = F)
library(dplyr, quietly = T, warn.conflicts = F)
library(plotly, quietly = T, warn.conflicts = F)
library(doParallel, quietly = T, warn.conflicts = F)
Sys.setlocale('LC_ALL','English')  
## [1] "LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252"

Getting data

Start by creating a directory to store data and download it from the web. While doing it, create a text file which states the time/timezone of download for reference purposes.

datadir <- './data'
trainingurl <- 'https://d396qusza40orc.cloudfront.net/predmachlearn/pml-training.csv'
testurl <- 'https://d396qusza40orc.cloudfront.net/predmachlearn/pml-testing.csv'
trainingpath <- paste0(datadir,'/pml-training.csv')
testpath <- paste0(datadir,'/pml-testing.csv')

if(!dir.exists(datadir)){
        dir.create(datadir)
}

if(!file.exists(trainingpath)){
        
        download.file(url = trainingurl,
                      destfile = trainingpath,
                      method = 'curl')
        download.file(url = testurl,
                      destfile = testpath,
                      method = 'curl')
        
        time <- as.character(Sys.time())
        timezone <- Sys.timezone()
        
        downloadinfo <- data.frame(list(time = time, 
                             format = "%Y-%m-%d %H:%M:%S",
                             timezone = timezone))
        
        write.table(x = downloadinfo,
                    file = paste0(datadir,'/downloadinfo.txt'),
                    row.names = F)
}

Brief Exploratory Data Analysis

Use fread to load data into R environment.

training <- fread(file = trainingpath, data.table = F,stringsAsFactors = T)
test <- fread(file = testpath, data.table = F,stringsAsFactors = T)

There is lots of NA values in this dataset, so to avoid any trouble I’m gonna filter out columns that contains any of them and select the variables of interest to the model. Our response variable is named ‘classe’ in the dataset. For the purpose of this project, I’m gonna select only the spacial variables as explanatory variables. I expect that those variables contains enough information about the movements for the random forest’s trees to be able to group them to each class. Same thing is applied to test set.

training1 <- training %>% 
        select(!where(anyNA)) %>%
        select(classe,ends_with(c('x','y','z')))
test1 <- test %>% 
        select(!where(anyNA)) %>%
        select(problem_id,ends_with(c('x','y','z')))

Let’s take a look at a 3dplot of some spatial variable to see if we are lucky to find any pattern that calls attention.

training1 %>% plot_ly(
        x=.$gyros_belt_x,
        z = .$gyros_belt_z, 
        y = .$gyros_belt_y,
        type = 'scatter3d',
        color = .$classe,
        mode = 'markers')

It seems like we were lucky here. We can see there is some coordinates where only the class E appears, same with class D. We hope that the random forest will be able to find more of those patterns and make a good model of it.

Building model

Let’s get to it. We are gonna use the caret package to build a random forest model with the variables we selected previously.

Random forests is a modification of bagging. It takes a “committee” of low performance trees to make each a prediction, then averages the results from them. This is especially good for variance of the model, because with the average, the noises of each tree can be “neutralized”, improving out of sample performance. Bias remains the same though, since each tree is identically distributed2.

For this to happen, one important aspect of the model is how data is gonna be resampled from the pool of data to each tree. We use 5-fold cross-validation, this way we expect each tree will have low bias and variance will be taken care through the cross validation.

Due to lake of computation power, number of trees is set to 200, even though this might hurt overall performance.

setControl <- trainControl(method = "cv",
number = 5,
allowParallel = TRUE)
model <- train(classe~.,
               data = training1,
               method = 'rf',
               ntree = 200,
               trControl = setControl)
model$finalModel
## 
## Call:
##  randomForest(x = x, y = y, ntree = 200, mtry = param$mtry) 
##                Type of random forest: classification
##                      Number of trees: 200
## No. of variables tried at each split: 2
## 
##         OOB estimate of  error rate: 0.89%
## Confusion matrix:
##      A    B    C    D    E class.error
## A 5572    2    0    5    1 0.001433692
## B   30 3749   18    0    0 0.012641559
## C    1   26 3393    1    1 0.008474576
## D    3    0   77 3133    3 0.025808458
## E    0    1    1    5 3600 0.001940671

That’s our final model, the estimated out of sample error is 0.87%, which is reasonably low.

Prediction

Now we are gonna use the model to predict the test set.

pred <- predict(model, test1)
cbind.data.frame(pred,test1$problem_id)
##    pred test1$problem_id
## 1     B                1
## 2     A                2
## 3     B                3
## 4     A                4
## 5     A                5
## 6     E                6
## 7     D                7
## 8     B                8
## 9     A                9
## 10    A               10
## 11    B               11
## 12    C               12
## 13    B               13
## 14    A               14
## 15    E               15
## 16    E               16
## 17    A               17
## 18    B               18
## 19    B               19
## 20    B               20

As we have seen previously, the estimated out of sample error is only 0.87%, so we estimated 100% of the test cases correctly.

  1. Velloso, E.; Bulling, A.; Gellersen, H.; Ugulino, W.; Fuks, H. Qualitative Activity Recognition of Weight Lifting Exercises. Proceedings of 4th International Conference in Cooperation with SIGCHI (Augmented Human ’13) . Stuttgart, Germany: ACM SIGCHI, 2013. Read more: http://groupware.les.inf.puc-rio.br/har#dataset#ixzz6c6x1JE00↩︎

  2. The Elements Of Statistical Learning, 2nd Edition, 2009. Acces Here.↩︎